In [1]:
!pip install https://github.com/ydataai/pandas-profiling/archive/master.zip

import pandas_profiling as pdpf

Collecting https://github.com/ydataai/pandas-profiling/archive/master.zip
  Downloading https://github.com/ydataai/pandas-profiling/archive/master.zip
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Building wheels for collected packages: pandas-profiling
  Building wheel for pandas-profiling (setup.py): started
  Building wheel for pandas-profiling (setup.py): finished with status 'done'
  Created wheel for pandas-profiling: filename=pandas_profiling-3.4.0-py2.py3-none-any.whl size=315268 sha256=32e27e9f79ca6bf14de02c9818101ff2692183316ab9c844115b8a6cae089497
  Stored in directory: C:\Users\sania\AppData\Local\Temp\pip-ephem-wheel-cache-jd994ful\wheels\de\d6\dc\61cc65cce70dda40583eef25ac13cbffb18dcd67295810e1c3
Successfully built pandas-profiling
Installing collected packages: pandas-profiling
  Attempting uninstall: pandas-profiling
    Found existing installation: pandas-profiling 3.3.1
    Uninstalling pandas-profiling-3.3.1:
      Successfully uninstalled pandas-profiling-3.3.1
Successfully installed pandas-profiling-3.4.0
In [5]:
import pandas as pd
import requests
import io
    
# Downloading the csv file from your GitHub account

#url = "C:\\Users\sania\Data analytics\Course materials 820\Latest census\Data collectionCensus22_initial.csv" 

# Reading the downloaded content and turning it into a pandas dataframe

df = pd.read_csv("C:\\Users\sania\Data analytics\Course materials 820\Latest census\Data collection\Census22_initial.csv")
In [11]:
# for the df in the question,

df['NOEMP']= df['NOEMP'].astype('str')
df['A_UNMEM'] = df['A_UNMEM'].astype('str')
df['FRMOTR'] = df['FRMOTR'].astype('str')
df['VET_YN']= df['VET_YN'].astype('str')
df['VET_QVA']= df['VET_QVA'].astype('str')
df['WKSWORK']= df['WKSWORK']. astype('int')
df['MIG_MTR3']= df['MIG_MTR3'].astype('str')
df['MIGSAME']= df['MIGSAME'].astype('str')
df['HHDREL']= df['HHDREL'].astype('str')
df['HHDFMX']= df['HHDFMX'].astype('str')
df['PARENT']= df['PARENT'].astype('str')
df['A_WKSTAT']= df['A_WKSTAT'].astype('str')
df['PRUNTYPE']= df['PRUNTYPE'].astype('str')
df['A_CLSWKR']= df['A_CLSWKR'].astype('str')
df['STATETAX_B']= df['STATETAX_B'].astype('int')
df['STATETAX_A']= df['STATETAX_A'].astype('int')
df['FILESTAT']= df['FILESTAT'].astype('str')
df['DIV_VAL']= df['DIV_VAL'].astype('int')
df['CAP_VAL']= df['CAP_VAL'].astype('int')
df['ERN_OTR']=  df['ERN_OTR'].astype('str')
df['A_HRSPAY']= df['A_HRSPAY'].astype('int')
df['A_MJOCC']= df['A_MJOCC'].astype('str')
df['A_MJIND']= df['A_MJIND'].astype('str')
df['A_HGA']= df['A_HGA'].astype('str')
df['A_MARITL'] = df['A_MARITL'].astype('str')
df['A_ENRLW']=  df['A_ENRLW'].astype('str')
df['A_SEX']= df['A_SEX'].astype('str')
df['PRDISFLG']= df['PRDISFLG'].astype('str')
df['PEMLR']= df['PEMLR'].astype('str')
df['PRCITSHP']= df['PRCITSHP'].astype('str')
df['PRDTRACE']= df['PRDTRACE'].astype('str')
df['PEFNTVTY']= df['PEFNTVTY'].astype('str')
df['PEMNTVTY']= df['PEMNTVTY'].astype('str')
df['PENATVTY']= df['PENATVTY'].astype('str')
df['PEHSPNON']= df['PEHSPNON'].astype('str')
df['OED_TYP3']= df['OED_TYP3'].astype('str')
df['OED_TYP2']= df['OED_TYP2'].astype('str')
df['OED_TYP1']= df['OED_TYP1'].astype('str')
df['PTOTVAL']= df['PTOTVAL'].astype('int')
df['A_AGE']= df['A_AGE'].astype('int')
In [12]:
df.dtypes
Out[12]:
OED_TYP1      object
OED_TYP2      object
OED_TYP3      object
PEHSPNON      object
PENATVTY      object
PEMNTVTY      object
PEFNTVTY      object
PRDTRACE      object
PRCITSHP      object
PEMLR         object
PRDISFLG      object
A_SEX         object
A_ENRLW       object
A_MARITL      object
A_HGA         object
A_AGE          int32
A_MJIND       object
A_MJOCC       object
A_HRSPAY       int32
ERN_OTR       object
CAP_VAL        int32
DIV_VAL        int32
FILESTAT      object
STATETAX_A     int32
STATETAX_B     int32
A_CLSWKR      object
PRUNTYPE      object
A_WKSTAT      object
PARENT        object
HHDFMX        object
HHDREL        object
MIGSAME       object
MIG_MTR3      object
WKSWORK        int32
VET_QVA       object
VET_YN        object
FRMOTR        object
A_UNMEM       object
NOEMP         object
PTOTVAL        int32
dtype: object
In [13]:
from pandas_profiling import ProfileReport
profile = ProfileReport(df, infer_dtypes=False, title="Pandas Profiling Report")
In [14]:
profile

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Out[14]:
























In [15]:



     


profile.to_file("EDA_Final.html")



     






















    
    








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In [16]:



     


!pyppeteer-install



     






















    
    






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 58%|#####7    | 78.8M/137M [00:13<00:18, 3.17Mb/s]
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 60%|######    | 82.2M/137M [00:14<00:16, 3.32Mb/s]
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 62%|######1   | 84.3M/137M [00:15<00:12, 4.33Mb/s]
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 80%|#######9  | 109M/137M [00:21<00:06, 4.41Mb/s]
 80%|########  | 110M/137M [00:21<00:05, 4.69Mb/s]
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 90%|######### | 124M/137M [00:24<00:02, 4.67Mb/s]
 91%|######### | 124M/137M [00:24<00:02, 4.87Mb/s]
 91%|#########1| 125M/137M [00:24<00:02, 4.92Mb/s]
 92%|#########1| 125M/137M [00:24<00:02, 4.85Mb/s]
 92%|#########1| 126M/137M [00:24<00:02, 4.95Mb/s]
 92%|#########2| 126M/137M [00:24<00:02, 5.02Mb/s]
 93%|#########2| 127M/137M [00:24<00:02, 4.71Mb/s]
 93%|#########3| 128M/137M [00:24<00:01, 5.06Mb/s]
 94%|#########3| 128M/137M [00:24<00:01, 4.96Mb/s]
 94%|#########4| 129M/137M [00:25<00:01, 5.18Mb/s]
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 95%|#########5| 131M/137M [00:25<00:01, 5.17Mb/s]
 96%|#########5| 131M/137M [00:25<00:01, 4.60Mb/s]
 96%|#########6| 132M/137M [00:25<00:01, 4.03Mb/s]
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 97%|#########6| 132M/137M [00:26<00:01, 3.13Mb/s]
 97%|#########6| 133M/137M [00:26<00:01, 3.13Mb/s]
 97%|#########7| 133M/137M [00:26<00:01, 2.58Mb/s]
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 98%|#########7| 134M/137M [00:26<00:00, 3.23Mb/s]
 98%|#########8| 135M/137M [00:26<00:00, 3.89Mb/s]
 99%|#########8| 135M/137M [00:26<00:00, 4.38Mb/s]
 99%|#########9| 136M/137M [00:26<00:00, 4.31Mb/s]
 99%|#########9| 136M/137M [00:27<00:00, 4.57Mb/s]
100%|#########9| 137M/137M [00:27<00:00, 4.70Mb/s]
100%|##########| 137M/137M [00:27<00:00, 5.04Mb/s]
[INFO] Beginning extraction
[INFO] Chromium extracted to: C:\Users\sania\AppData\Local\pyppeteer\pyppeteer\local-chromium\588429
























In [ ]: